Graphs: Algorithms and Mining

Contents: 1. Basics of graph theory. 2. Trees. 3. Network measures. 4. Graphs and pattern. 5. Graph clustering. 6. Information diffusion in a network. 7. Graph similarity and alignment. 8. Graph neural networks. 9. Selected topics in graphs: algorithms and mining. 10. Emerging trends in graph theory.

This book refers to the computational techniques used to analyse, extract insights from, and manipulate graph data structures. These techniques are essential for understanding the structure, properties, and relationships within graphs, which are prevalent in various real-world applications. Graph algorithms and mining play a crucial role in understanding the structure and dynamics of complex networks, enabling applications such as social network analysis, recommendation systems, network security, and biological network analysis. Learning a course that covers topics such as graph theory, data mining, distributed computing, text mining, and web search is crucial for several reasons, and its real -world applications are extensive.